Neuropsychiatric lupus or not? Cerebral hypoperfusion by perfusion-weighted MRI in normal-appearing white matter in primary neuropsychiatric lupus erythematosus

Prediction model for developing neuropsychiatric systemic lupus erythematosus in lupus patients

OBJECTIVE

This study aimed to construct a predictive model for assessing the risk of development of neuropsychiatric systemic lupus erythematosus (NPSLE) among patients with SLE based on clinical, laboratory, and meteorological data.

METHODS

A total of 2232 SLE patients were included and were randomly assigned into training and validation sets. Variables such as clinical and laboratory data and local meteorological data were screened by univariate and least absolute shrinkage and selection operator (LASSO) logistic regression modelling. After 10-fold cross-validation, the predictive model was built by multivariate logistic regression, and a nomogram was constructed to visualize the risk of NPSLE. The efficacy and accuracy of the model were assessed by receiver operating characteristic (ROC) curve and calibration curve analysis. Net clinical benefit was assessed by decision curve analysis.

RESULTS

Variables that were included in the predictive model were anti-dsDNA, anti-SSA, lymphocyte count, hematocrit, erythrocyte sedimentation rate, pre-albumin, retinol binding protein, creatine kinase isoenzyme MB, Nterminal brain natriuretic peptide precursor, creatinine, indirect bilirubin, fibrinogen, hypersensitive C-reactive protein, CO, and mild contamination. The nomogram showed a broad prediction spectrum; the area under the curve (AUC) was 0.895 (0.858-0.931) for the training set and 0.849 (0.783-0.916) for the validation set.

CONCLUSION

The model exhibits good predictive performance and will confer clinical benefit in NPSLE risk calculation. Key Points • Clinical, laboratory, and meteorological data were incorporated into a predictive model for neuropsychiatric systemic lupus erythematosus (NPSLE) in SLE patients. • Anti-dsDNA, anti-SSA, LYM, HCT, ESR, hsCRP, IBIL, PA, RBP, CO, Fib, NT-proBNP, Crea, CO, and mild contamination are predictors of the development of NPSLE and may have potential for research. • The nomogram has good predictive performance and clinical value and can be used to guide clinical diagnosis and treatment.

Prevalence and identification of neuropsychiatric symptoms in systemic autoimmune rheumatic diseases: an international mixed methods study

OBJECTIVE

A limited range of neuropsychiatric symptoms have been reported in systemic autoimmune rheumatic diseases (SARDs), with varied symptom prevalence. This study aimed to investigate a wider range of potential symptoms than previous studies, compare patient self-reports with clinician estimates, and explore barriers to symptom identification.

METHODS

Mixed methods were used. Data from SARDs patients (n = 1853) were compared with controls (n = 463) and clinicians (n = 289). In-depth interviews (n = 113) were analysed thematically. Statistical tests compared means of survey items between patients and controls, 8 different SARD groups, and clinician specialities.

RESULTS

Self-reported lifetime prevalences of all 30 neuropsychiatric symptoms investigated (including cognitive, sensorimotor and psychiatric) were significantly higher in SARDs than controls. Validated instruments assessed 55% of SARDs patients as currently having depression and 57% anxiety. Barriers to identifying neuropsychiatric symptoms included: (i) limits to knowledge, guidelines, objective tests and inter-speciality cooperation; (ii) subjectivity, invisibility and believability of symptoms; and (iii) under-eliciting, under-reporting and under-documenting. A lower proportion of clinicians (4%) reported never/rarely asking patients about mental health symptoms than the 74% of patients who reported never/rarely being asked in clinic (P < 0.001). Over 50% of SARDs patients had never/rarely reported their mental health symptoms to clinicians, a proportion underestimated at <10% by clinicians (P < 0.001).

CONCLUSION

Neuropsychiatric symptom self-reported prevalences are significantly higher in SARDs than controls, and are greatly underestimated by most clinicians. Research relying on medical records and current guidelines is unlikely to accurately reflect patients' experiences of neuropsychiatric symptoms. Improved inter-speciality communication and greater patient involvement is needed in SARD care and research.

Ectopic CD4+ T cells in choroid plexus mediate neuropsychiatric lupus symptoms in mice via interferon-γ induced microglia activation

Neuropsychiatric systemic lupus erythematosus (NPSLE) is a disabling and potentially life-threatening complication of SLE. This study aims to investigate whether ectopic CD4+ T cells in the choroid plexus mediate NPSLE in mice. Intracerebroventricular (ICV) injection of anti-CD4 antibody effectively depleted CP-resident CD4+ T cells and alleviated NPSLE-like symptoms in MRL/lpr mice. Following ICV injection, the majority of isolated lupus CD4+ T cells from donor MRL/lpr mice predominantly stayed in the CP for at least 28 days in recipient C57BL/6 mice, while nearly all isolated CD4+ T cells from MRL/MpJ mice disappeared within 7 days. ICV injection of lupus CD4+ T cells resulted in NPSLE-like symptoms, including impaired behavioral performances, increased microglial activation, and abnormal microstructure changes. Flow cytometry analysis revealed that the majority of isolated lupus CD4+ T cells were positive for IFN-γ. Neutralizing intracerebral IFN-γ alleviated NPSLE-like symptoms in MRL/lpr mice. Moreover, ICV injection of anti-IFN-γ antibody or microglial depletion by PLX3397 benefited most NPSLE-like symptoms in lupus CD4+ T-treated mice, while ICV injection of IFN-γ mimicked most NPSLE-like symptoms. In conclusion, CP-resident lupus CD4+ T cells contribute to NPSLE-like symptoms in mice via Interferon-γ induced microglia activation. Depleting CP-resident lupus CD4+ T cells, interferon-γ, or activated microglia may be potential therapeutic targets for NPSLE.

Cerebral Microstructural and Microvascular Changes in Non-Neuropsychiatric Systemic Lupus Erythematosus: A Study Using Diffusion Kurtosis Imaging and 3D Pseudo-Continuous Arterial Spin Labeling

Purpose

The purpose of this study was to observe cerebral microstructure and microcirculation features, as well as changes in white matter (WM) and gray matter (GM) among patients with non-neuropsychiatric systemic lupus erythematosus (non-NPSLE).

Methods

We compared 36 female patients with non-NPSLE and 20 age- and gender-matched healthy controls (HCs) who underwent 3.0T MRI imaging with diffusion kurtosis imaging (DKI) and 3D pseudo-continuous Arterial Spin Labeling (pCASL). Mean kurtosis (MK), mean kurtosis tensor (MKT), and cerebral blood flow (CBF) values were obtained from 25 brain regions, including WM and GM. We analyzed the correlation between imaging indicators and clinical data.

Results

When compared with HCs, patients with non-NPSLE had reduced MK and MKT values in regional WM, deep GM, and the left frontal lobe cortical GM, and increased CBF in the right parietal lobe WM and right semioval center (SOC). The MK and MKT values were weakly correlated with CBF in some regions, including WM and GM. Complement 3 (C3) and Complement 4 (C4) showed a weak positive correlation with MK and MKT in some regions, including WM and deep GM, while platelet (PLT) was positively correlated with MKT in the left frontal lobe WM; dsDNA antibody was correlated negatively with MK in the right occipital lobe WM; and erythrocyte sedimentation rate (ESR) was correlated negatively with CBF in the left SOC.

Conclusion

Our findings revealed the presence of brain microstructural and microvascular abnormalities in non-NPSLE patients, indicating microstructural damage in the cortical GM, which was less commonly reported. We found DKI and pCASL useful in detecting early brain lesions, and MK was a more sensitive and beneficial indicator.

Depression in systemic lupus erythematosus: A manifestation of microcirculation dysfunction?

OBJECTIVES

Depression is highly prevalent among systemic lupus erythematosus (SLE) patients. Brain hypoperfusion in neuropsychiatric SLE patients might be associated with emotional difficulties. However, no previous study examined possible associations of depression with brain oxygenation during a mild physical stress in non-neuropsychiatric SLE patients. Our study aimed to identify possible differences in cerebral oxygenation during exercise in SLE patients with and without depressive symptoms using near-infrared spectroscopy (NIRS) and examine possible underlying mechanisms through evaluation of vascular cell adhesion molecule 1 (VCAM-1) levels.

METHODS

SLE patients without a known neuropsychiatric history or treatment with antidepressants or antipsychotic drugs were enrolled. Participants were assigned into groups based on Beck's Depression Inventory I (BDI-I). Patients with BDI-I score ≥10 comprised the SLE-depression group and those with BDI-I score <9 the SLE-non-depression group. All participants underwent a protocol involving a seated rest, a 3-min handgrip exercise (at 30% of maximal strength), and a 3-min recovery. NIRS was used to monitor changes in cerebral oxygenated hemoglobin (O2Hb), deoxygenated (HHb), and total hemoglobin (tHb). VCAM-1 levels were measured in serum samples.

RESULTS

Twenty-three patients were enrolled. During exercise, the SLE-depression group exhibited a significantly lower increase in cerebral O2Hb [(peak-O2Hb (p = 0.039); O2Hb-area under the curve, AUC, p = 0.027) vs. SLE-non-depression group. BDI-I score was inversely correlated with AUC (rho = -0.493, p = 0.017) and positively correlated with VCAM-1 levels (rho = 0.501, p = 0.034).

CONCLUSION

This study suggests a possible association between emotional abnormalities and microvascular impairment (cerebral oxygenation and endothelial dysfunction) in SLE However, larger studies are needed to confirm these results.

Abnormal cerebral hemodynamics and blood-brain barrier permeability detected with perfusion MRI in systemic lupus erythematosus patients

OBJECTIVE

Dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) has previously shown alterations in cerebral perfusion in patients with systemic lupus erythematosus (SLE). However, the results have been inconsistent, in particular regarding neuropsychiatric (NP) SLE. Thus, we investigated perfusion-based measures in different brain regions in SLE patients with and without NP involvement, and additionally, in white matter hyperintensities (WMHs), the most common MRI pathology in SLE patients.

MATERIALS AND METHODS

We included 3 T MRI images (conventional and DSC) from 64 female SLE patients and 19 healthy controls (HC). Three different NPSLE attribution models were used: the Systemic Lupus International Collaborating Clinics (SLICC) A model (13 patients), the SLICC B model (19 patients), and the American College of Rheumatology (ACR) case definitions for NPSLE (38 patients). Normalized cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transit time (MTT) were calculated in 26 manually drawn regions of interest and compared between SLE patients and HC, and between NPSLE and non-NPSLE patients. Additionally, normalized CBF, CBV and MTT, as well as absolute values of the blood-brain barrier leakage parameter (K2) were investigated in WMHs compared to normal appearing white matter (NAWM) in the SLE patients.

RESULTS

After correction for multiple comparisons, the most prevalent finding was a bilateral significant decrease in MTT in SLE patients compared to HC in the hypothalamus, putamen, right posterior thalamus and right anterior insula. Significant decreases in SLE compared to HC were also found for CBF in the pons, and for CBV in the bilateral putamen and posterior thalamus. Significant increases were found for CBF in the posterior corpus callosum and for CBV in the anterior corpus callosum. Similar patterns were found for both NPSLE and non-NPSLE patients for all attributional models compared to HC. However, no significant perfusion differences were revealed between NPSLE and non-NPSLE patients regardless of attribution model. The WMHs in SLE patients showed a significant increase in all perfusion-based metrics (CBF, CBV, MTT and K2) compared to NAWM.

CONCLUSION

Our study revealed perfusion differences in several brain regions in SLE patients compared to HC, independently of NP involvement. Furthermore, increased K2 in WMHs compared to NAWM may indicate blood-brain barrier dysfunction in SLE patients. We conclude that our results show a robust cerebral perfusion, independent from the different NP attribution models, and provide insight into potential BBB dysfunction and altered vascular properties of WMHs in female SLE patients. Despite SLE being most prevalent in females, a generalization of our conclusions should be avoided, and future studies including all sexes are needed.

The conundrum of neuropsychiatric systemic lupus erythematosus: Current and novel approaches to diagnosis

Recognising neuropsychiatric involvement by systemic lupus erythematosus (SLE) is of growing importance, however many barriers to this exist at multiple levels of our currently available diagnostic algorithms that may ultimately delay its diagnosis and subsequent treatment. The heterogeneous and non-specific clinical syndromes, serological and cerebrospinal fluid (CSF) markers and neuroimaging findings that often do not mirror disease activity, highlight important research gaps in the diagnosis of neuropsychiatric SLE (NPSLE). Formal neuropsychological assessments or the more accessible screening metrics may also help improve objective recognition of cognitive or mood disorders. Novel serum and CSF markers, including autoantibodies, cytokines and chemokines have also shown increasing utility as part of diagnosis and monitoring, as well as in distinguishing NPSLE from SLE patients without SLE-related neuropsychiatric manifestations. Novel neuroimaging studies also expand upon our existing strategy by quantifying parameters that indicate microarchitectural integrity or provide an assessment of neuronal function. Some of these novel markers have shown associations with specific neuropsychiatric syndromes, suggesting that future research move away from considering NPSLE as a single entity but rather into its individually recognized neuropsychiatric manifestations. Nevertheless, it is likely that a composite panel of these investigations will be needed to better address the gaps impeding recognition of neuropsychiatric involvement by SLE.

Cognitive dysfunction in SLE: An understudied clinical manifestation

Neuropsychiatric lupus (NPSLE) is a debilitating manifestation of SLE which occurs in a majority of SLE patients and has a variety of clinical manifestations. In the central nervous system, NPSLE may result from ischemia or penetration of inflammatory mediators and neurotoxic antibodies through the blood brain barrier (BBB). Here we focus on cognitive dysfunction (CD) as an NPSLE manifestation; it is common, underdiagnosed, and without specific therapy. For a very long time, clinicians ignored cognitive dysfunction and researchers who might be interested in the question struggled to find an approach to understanding mechanisms for this manifestation. Recent years, however, propelled by a more patient-centric approach to disease, have seen remarkable progress in our understanding of CD pathogenesis. This has been enabled through the use of novel imaging modalities and numerous mouse models. Overall, these studies point to a pivotal role of an impaired BBB and microglial activation in leading to neuronal injury. These insights suggest potential therapeutic modalities and make possible clinical trials for cognitive impairment.

Structural and Functional Characterization of Gray Matter Alterations in Female Patients With Neuropsychiatric Systemic Lupus

Objective

To investigate morphological and functional alterations within gray matter (GM) in female patients with neuropsychiatric systemic lupus (NPSLE) and to explore their clinical significance.

Methods

54 female patients with SLE (30 NPSLE and 24 non-NPSLE) and 32 matched healthy controls were recruited. All subjects received a quantitative MRI scan (FLAIR, 3DT1, resting-state functional MRI). GM volume (GMV), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and degree of centrality (DC) were obtained. Between-group comparison, clinical correlation, and discrimination of NPSLE from non-NPSLE were achieved by voxel-based analysis, cerebellar seed-based functional connectivity analysis, regression analysis, and support vector machine (SVM), respectively.

Results

Patients with NPSLE showed overt subcortical GM atrophy without significantly abnormal brain functions in the same region compared with controls. The dysfunction within the left superior temporal gyri (L-STG) was found precede the GM volumetric loss. The function of the nodes in default mode network (DMN) and salience network (SN) were weakened in NPSLE patients compared to controls. The function of the cerebellar posterior lobes was significantly activated in non-NPSLE patients but attenuated along with GM atrophy and presented higher connectivity with L-STG and DMN in NPSLE patients, while the variation of the functional activities in the sensorimotor network (SMN) was the opposite. These structural and functional alterations were mainly correlated with disease burden and anti-phospholipid antibodies (aPLs) (r ranges from -1.53 to 1.29). The ReHos in the bilateral cerebellar posterior lobes showed high discriminative power in identifying patients with NPSLE with accuracy of 87%.

Conclusion

Patients with NPSLE exhibit both structural and functional alterations in the GM of the brain, which especially involved the deep GM, the cognitive, and sensorimotor regions, reflecting a reorganization to compensate for the disease damage to the brain which was attenuated along with pathologic burden and cerebral vascular risk factors. The GM within the left temporal lobe may be one of the direct targets of lupus-related inflammatory attack. The function of the cerebellar posterior lobes might play an essential role in compensating for cortical functional disturbances and may contribute to identifying patients with suspected NPSLE in clinical practice.

First Diagnosis of Systemic Lupus Erythematosus in Hospitalized Patients: Clinical Phenotypes and Pitfalls for the Non-Specialist

BACKGROUND

Prompt recognition of systemic lupus erythematosus (SLE) in hospitalized patients presenting with severe disease is essential to initiate treatment. We sought to characterize the phenotype of hospitalized patients with new-onset SLE and estimate potential diagnostic delays.

METHODS

An observational study of 855 patients ("Attikon" SLE cohort). Clinical phenotype was categorized according to the leading manifestation that led to hospitalization. Disease features, time to diagnosis, classification criteria, and the SLE Risk Probability Index (SLERPI) were recorded for each patient.

RESULTS

There were 191 patients (22.3% of the total cohort) hospitalized due to manifestations eventually attributed to SLE. Main causes of admission were neuropsychiatric syndromes (21.4%), cytopenias (17.8%), nephritis (17.2%), and thrombotic events (16.2%). Although 79.5% of patients were diagnosed within 3 months from hospitalization, in 39 patients diagnosis was delayed, particularly in those with hematological manifestations. At hospitalization, a SLERPI >7 (indicating high probability for SLE) was found in 87.4% of patients. Patients missed by the SLERPI had fever, thrombotic or neuropsychiatric manifestations not included in the algorithm. Lowering the SLERPI threshold to 5 in patients with fever or thrombotic events increased the diagnostic rate from 88.8% to 97.9% in this subgroup, while inclusion of all neuropsychiatric events yielded no additional diagnostic value.

CONCLUSION

One in five patients with new-onset SLE manifest disease presentations required hospitalization. Although early diagnosis was achieved in the majority of cases, in approximately 20%, diagnosis was delayed. A lower SLERPI cut-off (≥5) in patients with fever or thrombosis could enhance early diagnosis.

Abnormal cortical thickness and structural covariance networks in systemic lupus erythematosus patients without major neuropsychiatric manifestations

BACKGROUND

Non-neuropsychiatric systemic lupus erythematosus (non-NPSLE) has been confirmed to have subtle changes in brain structure before the appearance of obvious neuropsychiatric symptoms. Previous literature mainly focuses on brain structure loss in non-NPSLE; however, the results are heterogeneous, and the impact of structural changes on the topological structure of patients' brain networks remains to be determined. In this study, we combined neuroimaging and network analysis methods to evaluate the changes in cortical thickness and its structural covariance networks (SCNs) in patients with non-NPSLE.

METHODS

We compare the cortical thickness of non-NPSLE patients (N=108) and healthy controls (HCs, N=88) using both surface-based morphometry (SBM) and regions of interest (ROI) methods, respectively. After that, we analyzed the correlation between the abnormal cortical thickness results found in the ROI method and a series of clinical features. Finally, we constructed the SCNs of two groups using the regional cortical thickness and analyzed the abnormal SCNs of non-NPSLE.

RESULTS

By SBM method, we found that cortical thickness of 34 clusters in the non-NPSLE group was thinner than that in the HC group. ROI method based on Destrieux atlas showed that cortical thickness of 57 regions in the non-NPSLE group was thinner than that in the HC group and related to the course of disease, autoantibodies, the cumulative amount of immunosuppressive agents, and cognitive psychological scale. In the SCN analysis, the cortical thickness SCNs of the non-NPSLE group did not follow the small-world attribute at a few densities, and the global clustering coefficient appeared to increase. The area under the curve analysis showed that there were significant differences between the two groups in clustering coefficient, degree, betweenness, and local efficiency. There are a total of seven hubs for non-NPSLE, and five hubs in HCs, the two groups do not share a common hub distribution.

CONCLUSION

Extensive and obvious reduction in cortical thickness and abnormal topological organization of SCNs are observed in non-NPSLE patients. The observed abnormalities may not only be the realization of brain damage caused by the disease, but also the contribution of the compensatory changes within the nervous system.

Cerebral Microstructure Analysis by Diffusion-Based MRI in Systemic Lupus Erythematosus: Lessons Learned and Research Directions

Diffusion-based magnetic resonance imaging (MRI) studies, namely diffusion-weighted imaging (DWI) and diffusion-tensor imaging (DTI), have been performed in the context of systemic lupus erythematosus (SLE), either with or without neuropsychiatric (NP) involvement, to deepen cerebral microstructure alterations. These techniques permit the measurement of the variations in random movement of water molecules in tissues, enabling their microarchitecture analysis. While DWI is recommended as part of the initial MRI assessment of SLE patients suspected for NP involvement, DTI is not routinely part of the instrumental evaluation for clinical purposes, and it has been mainly used for research. DWI and DTI studies revealed less restricted movement of water molecules inside cerebral white matter (WM), expression of a global loss of WM density, occurring in the context of SLE, prevalently, but not exclusively, in case of NP involvement. More advanced studies have combined DTI with other quantitative MRI techniques, to further characterize disease pathogenesis, while brain connectomes analysis revealed structural WM network disruption. In this narrative review, the authors provide a summary of the evidence regarding cerebral microstructure analysis by DWI and DTI studies in SLE, focusing on lessons learned and future research perspectives.

Screening for cognitive impairment in systemic lupus erythematosus: Application of the Montreal Cognitive Assessment (MoCA) in a Greek patient sample

BACKGROUND

Cognitive impairment (CI) is one of the most frequent neuropsychiatric manifestations of systemic lupus erythematosus (SLE). Given that extensive neuropsychological testing is not always feasible in routine clinical practice, brief cognitive screening tools are desirable. The aim of this study was to evaluate the Montreal Cognitive Assessment (MoCA) as a screening tool for CI in SLE.

METHODS

Consecutive SLE patients followed at a single centre were evaluated using MoCA and an extensive neuropsychological test battery (NPT), including the Digits Forward and Digits Backwards, Rey Auditory Verbal Learning Memory Test, Trail Making Test, Stroop Colour-Word Test, Semantic and Phonetic Verbal Fluency tests and a 25-problem version of the General Adult Mental Ability test. The criterion validity of MoCA was assessed through receiver operating characteristic (ROC) analyses using three different case definitions: i) against normative population data, ii) and iii) against average performance of a comparison group of rheumatoid arthritis (RA) patients, to adjust for possible confounding effects of chronic illness and inflammatory processes on cognitive performance. The effect of patient-related (age, years of education, anxiety, depression, fatigue and pain) and disease-related (activity, damage, age at diagnosis, disease duration, use of glucocorticoid, psychotropic and pain medication) parameters on the MoCA was examined.

RESULTS

A total of 71 SLE patients were evaluated. MoCA significantly correlated with all NPT scores and was affected by education level (p < 0.001), but not by other demographic or clinical variables. The optimal cutoff for detecting CI, as defined on the basis of normative population data, was 23/30 points, demonstrating 73% sensitivity and 75% specificity. A cutoff of 22/30 points, using neuropsychological profiles of the RA group as inflammatory disease controls, exhibited higher sensitivity (100%, based on both definitions) and specificity (87% and 90%, depending on the definition). The standard cutoff of 26/30 points displayed excellent sensitivity (91-100%) with significant expenses in specificity (43-45%).

CONCLUSION

The MoCA is an easily applied tool, which appears to be reliable for identifying CI in SLE patients. The standard cutoff score (26/30) ensures excellent sensitivity while lower cutoff scores (22-23/30) may, also, provide higher specificity.

Headache in patients with systemic lupus erythematosus: characteristics, brain MRI patterns, and impact

Background

The prevalence of primary headache in patients with systemic lupus erythematosus (SLE) varies widely and whether it should be attributed to neurological involvement is controversial. We aimed to investigate the prevalence and characteristics of headache in SLE patients, describe its association with disease-related variables and brain imaging, and explore its impact on life.

Results

The overall prevalence of headache was 54.4%, and migraine was the most common type among headache sufferers (48.4%). Headache severity (VAS) and impact (HIT-6) correlated with SLEDAI-2K (P = 0.019 and P < 0.001, respectively) as well as with each other (P = 0.006). Brain imaging abnormalities were found in 25.8%, with white-matter hyperintensities (WMH) being the most frequent pathology. Musculoskeletal manifestations, positive anti-phospholipid (aPL) antibodies, and SLEDAI score ≥ 13.5 were identified as predictors of headache.

Conclusion

Primary headache, especially migraine, is a common feature of patients with SLE, and its presence is associated with negative impact on quality of life. Musculoskeletal features, aPL positivity, and overall disease activity appear to predict primary headache in SLE.

Lupus or not? SLE Risk Probability Index (SLERPI): a simple, clinician-friendly machine learning-based model to assist the diagnosis of systemic lupus erythematosus

Objectives

Diagnostic reasoning in systemic lupus erythematosus (SLE) is a complex process reflecting the probability of disease at a given timepoint against competing diagnoses. We applied machine learning in well-characterised patient data sets to develop an algorithm that can aid SLE diagnosis.

Methods

From a discovery cohort of randomly selected 802 adults with SLE or control rheumatologic diseases, clinically selected panels of deconvoluted classification criteria and non-criteria features were analysed. Feature selection and model construction were done with Random Forests and Least Absolute Shrinkage and Selection Operator-logistic regression (LASSO-LR). The best model in 10-fold cross-validation was tested in a validation cohort (512 SLE, 143 disease controls).

Results

A novel LASSO-LR model had the best performance and included 14 variably weighed features with thrombocytopenia/haemolytic anaemia, malar/maculopapular rash, proteinuria, low C3 and C4, antinuclear antibodies (ANA) and immunologic disorder being the strongest SLE predictors. Our model produced SLE risk probabilities (depending on the combination of features) correlating positively with disease severity and organ damage, and allowing the unbiased classification of a validation cohort into diagnostic certainty levels (unlikely, possible, likely, definitive SLE) based on the likelihood of SLE against other diagnoses. Operating the model as binary (lupus/not-lupus), we noted excellent accuracy (94.8%) for identifying SLE, and high sensitivity for early disease (93.8%), nephritis (97.9%), neuropsychiatric (91.8%) and severe lupus requiring immunosuppressives/biologics (96.4%). This was converted into a scoring system, whereby a score >7 has 94.2% accuracy.

Conclusions

We have developed and validated an accurate, clinician-friendly algorithm based on classical disease features for early SLE diagnosis and treatment to improve patient outcomes.

Anxiety and depression severity in neuropsychiatric SLE are associated with perfusion and functional connectivity changes of the frontolimbic neural circuit: a resting-state f(unctional) MRI study

OBJECTIVE

To examine the hypothesis that perfusion and functional connectivity disturbances in brain areas implicated in emotional processing are linked to emotion-related symptoms in neuropsychiatric SLE (NPSLE).

METHODS

Resting-state fMRI (rs-fMRI) was performed and anxiety and/or depression symptoms were assessed in 32 patients with NPSLE and 18 healthy controls (HC). Whole-brain time-shift analysis (TSA) maps, voxel-wise global connectivity (assessed through intrinsic connectivity contrast (ICC)) and within-network connectivity were estimated and submitted to one-sample t-tests. Subgroup differences (high vs low anxiety and high vs low depression symptoms) were assessed using independent-samples t-tests. In the total group, associations between anxiety (controlling for depression) or depression symptoms (controlling for anxiety) and regional TSA or ICC metrics were also assessed.

RESULTS

Elevated anxiety symptoms in patients with NPSLE were distinctly associated with relatively faster haemodynamic response (haemodynamic lead) in the right amygdala, relatively lower intrinsic connectivity of orbital dlPFC, and relatively lower bidirectional connectivity between dlPFC and vmPFC combined with relatively higher bidirectional connectivity between ACC and amygdala. Elevated depression symptoms in patients with NPSLE were distinctly associated with haemodynamic lead in vmPFC regions in both hemispheres (lateral and medial orbitofrontal cortex) combined with relatively lower intrinsic connectivity in the right medial orbitofrontal cortex. These measures failed to account for self-rated, milder depression symptoms in the HC group.

CONCLUSION

By using rs-fMRI, altered perfusion dynamics and functional connectivity was found in limbic and prefrontal brain regions in patients with NPSLE with severe anxiety and depression symptoms. Although these changes could not be directly attributed to NPSLE pathology, results offer new insights on the pathophysiological substrate of psychoemotional symptomatology in patients with lupus, which may assist its clinical diagnosis and treatment.

Treatment of neuropsychiatric systemic lupus erythematosus: clinical challenges and future perspectives

Introduction: Neuropsychiatric (NP) involvement represents an emerging frontier in systemic lupus erythematosus (SLE), posing significant challenges due to its clinical diversity and obscure pathophysiology. The authors herein discuss selected aspects in the management of NPSLE based on existing literature and our experience, aiming to facilitate routine medical care.Areas covered: Research related to diagnosis, neuroimaging, treatment and outcome is discussed, focusing on data published in PubMed during the last 5 years. Selected translational studies of clinical relevance are included.Expert opinion: Identification of NPSLE patients who may benefit from appropriate treatment can be facilitated by attribution algorithms. Immunosuppressants are typically indicated in recurrent seizures, optic neuritis, myelopathy, psychosis and peripheral nerve disease, although a low threshold is recommended for cerebrovascular disease and other NP manifestations, especially when SLE is active. With the exception of stroke with positive antiphospholipid antibodies, anti-coagulation is rarely indicated in other syndromes. Refractory NPSLE can be treated with rituximab, whereas the role of other biologics remains unknown. Advances in the fields of biomarkers, neuroimaging for brain structural, perfusion or functional abnormalities, and design of novel compounds targeting not only systemic autoimmunity but also inflammatory and regenerative pathways within the nervous system, hold promise for optimizing NPSLE management.

Lupus or not? SLE Risk Probability Index (SLERPI): a simple, clinician-friendly machine learning-based model to assist the diagnosis of systemic lupus erythematosus

OBJECTIVES

Diagnostic reasoning in systemic lupus erythematosus (SLE) is a complex process reflecting the probability of disease at a given timepoint against competing diagnoses. We applied machine learning in well-characterised patient data sets to develop an algorithm that can aid SLE diagnosis.

METHODS

From a discovery cohort of randomly selected 802 adults with SLE or control rheumatologic diseases, clinically selected panels of deconvoluted classification criteria and non-criteria features were analysed. Feature selection and model construction were done with Random Forests and Least Absolute Shrinkage and Selection Operator-logistic regression (LASSO-LR). The best model in 10-fold cross-validation was tested in a validation cohort (512 SLE, 143 disease controls).

RESULTS

A novel LASSO-LR model had the best performance and included 14 variably weighed features with thrombocytopenia/haemolytic anaemia, malar/maculopapular rash, proteinuria, low C3 and C4, antinuclear antibodies (ANA) and immunologic disorder being the strongest SLE predictors. Our model produced SLE risk probabilities (depending on the combination of features) correlating positively with disease severity and organ damage, and allowing the unbiased classification of a validation cohort into diagnostic certainty levels (unlikely, possible, likely, definitive SLE) based on the likelihood of SLE against other diagnoses. Operating the model as binary (lupus/not-lupus), we noted excellent accuracy (94.8%) for identifying SLE, and high sensitivity for early disease (93.8%), nephritis (97.9%), neuropsychiatric (91.8%) and severe lupus requiring immunosuppressives/biologics (96.4%). This was converted into a scoring system, whereby a score >7 has 94.2% accuracy.

CONCLUSIONS

We have developed and validated an accurate, clinician-friendly algorithm based on classical disease features for early SLE diagnosis and treatment to improve patient outcomes.

Advanced neuroimaging in neuropsychiatric systemic lupus erythematosus

Neuropsychiatric lupus (NPSLE) comprises a disparate collection of syndromes affecting the central and peripheral nervous systems. Progress in the attribution of neuropsychiatric syndromes to SLE-related mechanisms and development of targeted treatment strategies has been impeded by a lack of objective imaging biomarkers that reflect specific neuropsychiatric syndromes and/or pathologic mechanisms. The present review addresses recent publications of neuroimaging techniques in NPSLE.

The management of neuropsychiatric lupus in the 21st century: still so many unmet needs?

Neuropsychiatric (NP) events occur in the majority of patients with SLE and predominantly affect the CNS in addition to the peripheral and autonomic systems. Approximately 30% of all NP events are attributable to SLE (NPSLE) and present most frequently around the time of SLE onset. NPSLE is associated with increased morbidity and mortality and the proposed pathogenesis includes both ischaemic and neuroinflammatory mechanisms. Following diagnosis and causal attribution, the treatment of NPSLE is tailored to the type of NP event, the predominant putative pathogenic pathway and the activity and severity of the clinical event. There is a dearth of controlled clinical trials to guide management, but therapeutic options include symptomatic, antithrombotic and immunosuppressive agents that are supported by observational cohort studies. Our objective was to review what is currently known about NPSLE and to identify deficiencies in diagnostic biomarkers, novel therapies and clinical trials for this manifestation of SLE.

Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach

Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients.

Update οn the diagnosis and management of systemic lupus erythematosus

Clinical heterogeneity, unpredictable course and flares are characteristics of systemic lupus erythematosus (SLE). Although SLE is-by and large-a systemic disease, occasionally it can be organ-dominant, posing diagnostic challenges. To date, diagnosis of SLE remains clinical with a few cases being negative for serologic tests. Diagnostic criteria are not available and classification criteria are often used for diagnosis, yet with significant caveats. Newer sets of criteria (European League Against Rheumatism (EULAR)/American College of Rheumatology (ACR) 2019) enable earlier and more accurate classification of SLE. Several disease endotypes have been recognised over the years. There is increased recognition of milder cases at presentation, but almost half of them progress overtime to more severe disease. Approximately 70% of patients follow a relapsing-remitting course, the remaining divided equally between a prolonged remission and a persistently active disease. Treatment goals include long-term patient survival, prevention of flares and organ damage, and optimisation of health-related quality of life. For organ-threatening or life-threatening SLE, treatment usually includes an initial period of high-intensity immunosuppressive therapy to control disease activity, followed by a longer period of less intensive therapy to consolidate response and prevent relapses. Management of disease-related and treatment-related comorbidities, especially infections and atherosclerosis, is of paramount importance. New disease-modifying conventional and biologic agents-used alone, in combination or sequentially-have improved rates of achieving both short-term and long-term treatment goals, including minimisation of glucocorticoid use.

Update on the pathogenesis of central nervous system lupus

PROPOSE OF REVIEW

Neuropsychiatric systemic lupus erythematosus (NPSLE) is an emerging frontier in lupus care encompassing a wide spectrum of clinical manifestations. Its pathogenesis remains poorly understood because of the complexity of pathophysiologic mechanisms involved and limited access to tissue. We highlight recent advances in the pathophysiology of neuropsychiatric lupus.

RECENT FINDINGS

Disruption of blood-brain barrier (BBB) facilitating entrance of neurotoxic antibodies into the central nervous system (CNS), neuroinflammation and cerebral ischemia are the key mechanisms. Disruption of the BBB may occur not only at the traditional BBB, but also at the blood-cerebrospinal fluid barrier. Certain autoantibodies, such as anti-N-methyl-D-aspartate receptors, antiribosomal P and antiphospholipid antibodies may cause injury in subsets of patients with diffuse neuropsychiatric disease. Activation of microglia via autoantibodies, interferon-a or other immune reactants, may amplify the inflammatory response and promote neuronal damage. New inflammatory pathways, such as TWEAK/Fn14, Bruton's tyrosine kinase, Nogo-a and ACE may represent additional potential targets of therapy. Novel neuroimaging techniques suggest alterations in brain perfusion and metabolism, increased concentration of neurometabolites, indicative of glial activation, vasculopathy and neuronal impairment.

SUMMARY

NPSLE encompasses a diverse phenotype with distinct pathogenic mechanisms, which could be targeted by novel therapies or repositioning of existing drugs.

CNS Vasculitis-An Overview of This Multiple Sclerosis Mimic: Clinical and MRI Implications

This article discusses central nervous system vasculitis, a clinical and MRI mimic of multiple sclerosis (MS). There is a paucity of discussion of vasculitis in the radiology literature, and many MS neurologists believe that vasculitis is underdiagnosed. Therefore, the authors hope that the readers will find this paper increases their knowledge about CNS vasculitis and improves their ability to differentiate MS from vasculitis.

Altered cerebral perfusion in children with Langerhans cell histiocytosis after chemotherapy

BACKGROUND AND PURPOSE

Children with Langerhans cell histiocytosis (LCH) may develop a wide array of neurological symptoms, but associated cerebral physiologic changes are poorly understood. We examined cerebral hemodynamic properties of pediatric LCH using arterial spin-labeling (ASL) perfusion magnetic resonance imaging (MRI).

MATERIALS AND METHODS

A retrospective study was performed in 23 children with biopsy-proven LCH. Analysis was performed on routine brain MRI obtained before or after therapy. Region of interest (ROI) methodology was used to determine ASL cerebral blood flow (CBF) (mL/100 g/min) in the following bilateral regions: angular gyrus, anterior prefrontal cortex, orbitofrontal cortex, dorsal anterior cingulate cortex, and hippocampus. Quantile (median) regression was performed for each ROI location. CBF patterns were compared between pre- and posttreatment LCH patients as well as with age-matched healthy controls.

RESULTS

Significantly reduced CBF was seen in posttreatment children with LCH compared to age-matched controls in angular gyrus (P = .046), anterior prefrontal cortex (P = .039), and dorsal anterior cingulate cortex (P = .023). Further analysis revealed dominant perfusion abnormalities in the right hemisphere. No significant perfusion differences were observed in the hippocampus or orbitofrontal cortex.

CONCLUSION

Perfusion in specific cerebral regions may be consistently reduced in children with LCH, and may represent effects of underlying disease physiology and/or sequelae of chemotherapy. Studies that combine a formal cognitive assessment and hemodynamic data may further provide insight into perfusion deficits associated with the disease and the potential neurotoxic effects in children treated by chemotherapy.

Broad Learning Enhanced 1H-MRS for Early Diagnosis of Neuropsychiatric Systemic Lupus Erythematosus

In this paper, we explore the potential of using the multivoxel proton magnetic resonance spectroscopy (¹H-MRS) to diagnose neuropsychiatric systemic lupus erythematosus (NPSLE) with the assistance of a support vector machine broad learning system (BL-SVM). We retrospectively analysed 23 confirmed patients and 16 healthy controls, who underwent a 3.0 T magnetic resonance imaging (MRI) sequence with multivoxel ¹H-MRS in our hospitals. One hundred and seventeen metabolic features were extracted from the multivoxel ¹H-MRS image. Thirty-three metabolic features selected by the Mann-Whitney U test were considered to have a statistically significant difference (p < 0.05). However, the best accuracy achieved by conventional statistical methods using these 33 metabolic features was only 77%. We turned to develop a support vector machine broad learning system (BL-SVM) to quantitatively analyse the metabolic features from ¹H-MRS. Although not all the individual features manifested statistics significantly, the BL-SVM could still learn to distinguish the NPSLE from the healthy controls. The area under the receiver operating characteristic curve (AUC), the sensitivity, and the specificity of our BL-SVM in predicting NPSLE were 95%, 95.8%, and 93%, respectively, by 3-fold cross-validation. We consequently conclude that the proposed system effectively and efficiently working on limited and noisy samples may brighten a noinvasive in vivo instrument for early diagnosis of NPSLE.

Regional cerebral perfusion correlates with anxiety in neuropsychiatric SLE: evidence for a mechanism distinct from depression

The study examined the hypothesis that hypoperfusion in brain areas known to be involved in emotional disturbances in primary psychiatric disorders is also linked to emotional difficulties in systemic lupus erythematosus (SLE) and that these are not secondary to the physical and social burden incurred by the disease. Nineteen SLE patients without overt neuropsychiatric manifestations (non-NPSLE), 31 NPSLE patients, and 23 healthy controls were examined. Dynamic susceptibility contrast MRI was used and cerebral blood flow and cerebral blood volume values were estimated in six manually selected regions of interest of brain regions suspected to play a role in anxiety and depression (dorsolateral prefrontal cortex, ventromedial prefrontal cortex, anterior cingulate cortex, hippocampi, caudate nuclei and putamen). NPSLE patients reported high rates of anxiety and depression symptomatology. Significantly reduced cerebral blood flow and cerebral blood volume values were detected in the NPSLE group compared to healthy controls in the dorsolateral prefrontal cortex and ventromedial prefrontal cortex, bilaterally. Within the NPSLE group, anxiety symptomatology was significantly associated with lower perfusion in frontostriatal regions and in the right anterior cingulate gyrus. Importantly, the latter associations appeared to be specific to anxiety symptoms, as they persisted after controlling for depression symptomatology and independent of the presence of visible lesions on conventional MRI. In conclusion, hypoperfusion in specific limbic and frontostriatal regions is associated with more severe anxiety symptoms in the context of widespread haemodynamic disturbances in NPSLE.

In an early SLE cohort the ACR-1997, SLICC-2012 and EULAR/ACR-2019 criteria classify non-overlapping groups of patients: use of all three criteria ensures optimal capture for clinical studies while their modification earlier classification and treatment

OBJECTIVES

Classification criteria are biased towards classifying long-standing disease. We compared the European League Against Rheumatism (EULAR)/American College of Rheumatology (ACR)-2019, Systemic Lupus International Collaborating Clinics (SLICC)-2012 and ACR-1997 criteria in an early (median 48 months) systemic lupus erythematosus (SLE) cohort.

METHODS

Patients diagnosed with SLE (n=690) or control diseases (n=401). Sensitivity, specificity of the criteria and time-to-classification were calculated. Modified classification algorithms were derived from a random 80% and validated in the remaining 20% of the dataset running multiple iterations.

RESULTS

At last assessment, sensitivities of ACR-1997, SLICC-2012 and EULAR/ACR-2019 criteria were 85.7%, 91.3% and 88.6%, with specificities 93.0%, 93.8% and 97.3%, respectively. Both SLICC and EULAR/ACR enabled earlier classification. Only 76.7% of patients with SLE met all three criteria suggesting non-overlapping groups. Notably, unclassified patients had high prevalence of British Isles Lupus Assessment Group moderate/severe manifestations (43.3%-60%) and SLICC/ACR organ damage (30%-50%). At diagnosis, criteria missed 25.6%-30.5% of patients. Modification of EULAR/ACR and SLICC algorithms to include hypocomplementaemia and/or positive anti-phospholipid antibodies as alternative entry criterion, and/or allow classification with fewer clinical criteria from multiple organs, increased their sensitivity at diagnosis (median 82.0% and 86.2%) and overall (93.7% and 97.1%) with modest decreases in specificity. Importantly, patients who were still missed by the modified criteria had lower incidence of major organ involvement, use of immunosuppressive/biological therapies and organ damage.

CONCLUSIONS

The SLICC and EULAR/ACR are more sensitive than the ACR and the EULAR/ACR criteria have superior specificity in early SLE, although patients with significant disease can be missed. Combination and/or modification of the classification algorithms may enhance their sensitivity, allowing earlier classification and treatment of more patients with high disease burden.

Different patterns of cerebral perfusion in SLE patients with and without neuropsychiatric manifestations

To investigate brain perfusion patterns in systemic lupus erythematosus (SLE) patients with and without neuropsychiatric systemic lupus erythematosus (NPSLE and non-NPSLE, respectively) and to identify biomarkers for the diagnosis of NPSLE using noninvasive three-dimensional (3D) arterial spin labeling (ASL). Thirty-one NPSLE and 24 non-NPSLE patients and 32 age- and sex-matched normal controls (NCs) were recruited. Three-dimensional ASL-MRI was applied to quantify cerebral perfusion. Whole brain, gray (GM) and white matter (WM), and voxel-based analysis (VBA) were performed to explore perfusion characteristics. Correlation analysis was performed to find the relationship between the perfusion measures, lesion volumes, and clinical variables. Receiver operating characteristic (ROC) analysis and support vector machine (SVM) classification were applied to differentiate NPSLE patients from non-NPSLE patients and healthy controls. Compared to NCs, NPSLE patients showed increased cerebral blood flow (CBF) within WM but decreased CBF within GM, while non-NPSLE patients showed increased CBF within both GM and WM. Compared to non-NPSLE patients, NPSLE patients showed significantly reduced CBF in the frontal gyrus, cerebellum, and corpus callosum. CBF within several brain regions such as cingulate and corpus callosum showed significant correlations with the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) and the Systemic Lupus International Collaborating Clinics (SLICC) damage index scores. ROC analysis showed moderate performance in distinguishing NPSLE from non-NPSLE patients with AUCs > 0.7, while SVM analysis demonstrated that CBF within the corpus callosum achieved an accuracy of 83.6% in distinguishing NPSLE from non-NPSLE patients. Different brain perfusion patterns were observed between NPSLE and non-NPSLE patients. CBF measured by noninvasive 3D ASL could be a useful biomarker for the diagnosis and disease monitoring of NPSLE and non-NPSLE patients.

Laboratory and Neuroimaging Biomarkers in Neuropsychiatric Systemic Lupus Erythematosus: Where Do We Stand, Where To Go?

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by multi-systemic involvement. Nervous system involvement in SLE leads to a series of uncommon and heterogeneous neuropsychiatric (NP) manifestations. Current knowledge on the underlying pathogenic processes and their subsequent pathophysiological changes leading to NP-SLE manifestations is incomplete. Several putative laboratory biomarkers have been proposed as contributors to the genesis of SLE-related nervous system damage. Alongside the laboratory biomarkers, several neuroimaging tools have shown to reflect the nature of tissue microstructural damage associated with SLE, and thus were suggested to contribute to the understanding of the pathophysiological changes and subsequently help in clinical decision making. However, the number of useful biomarkers in NP-SLE in clinical practice is disconcertingly modest. In some cases it is not clear whether the biomarker is truly involved in pathogenesis, or the result of non-specific pathophysiological changes in the nervous system (e.g., neuroinflammation) or whether it is the consequence of a concomitant underlying abnormality related to SLE activity. In order to improve the diagnosis of NP-SLE and provide a better targeted care to these patients, there is still a need to develop and validate a range of biomarkers that reliably capture the different aspects of disease heterogeneity. This article critically reviews the current state of knowledge on laboratory and neuroimaging biomarkers in NP-SLE, discusses the factors that need to be addressed to make these biomarkers suitable for clinical application, and suggests potential future research paths to address important unmet needs in the NP-SLE field.

Diagnostic approach to neuropsychiatric lupus erythematosus: what should we do?

Neuropsychiatric systemic lupus erythematosus is a diagnostic challenge due to the multifarious neurological and psychiatric manifestations that define it but, when suspected, diagnostic imaging can give a fundamental help. The advancements and variety of neuroimaging techniques allow us to perform more and more accurate evaluations of structure, perfusion, and metabolism of the brain and to detect cerebral and spinal lesions. Moreover, vascular districts of the neck and the brain, as well as the electrical brain and peripheral muscle activity may be accurately investigated, thus giving us a wide panoramic view. Although magnetic resonance is recognized as a fundamental neuroimaging technique to reach a correct diagnosis, the juxtaposition of other diagnostic techniques has improved the possibility to make diagnoses but has also increased the confusion about deciding which of them to use and when. Our aim was to combine the number of available techniques with the need to simplify the diagnostic path. Therefore, through the construction of an algorithm from an evidence based approach, we believe we are providing some added improvements to facilitate and expedite the diagnosis of NPSLE.